Habesha@DravidianLangTech 2024: Detecting Fake News Detection in Dravidian Languages using Deep Learning

Mesay Yigezu, Olga Kolesnikova, Grigori Sidorov, Alexander Gelbukh


Abstract
This research tackles the issue of fake news by utilizing the RNN-LSTM deep learning method with optimized hyperparameters identified through grid search. The model’s performance in multi-label classification is hindered by unbalanced data, despite its success in binary classification. We achieved a score of 0.82 in the binary classification task, whereas in the multi-class task, the score was 0.32. We suggest incorporating data balancing techniques for researchers who aim to further this task, aiming to improve results in managing a variety of information.
Anthology ID:
2024.dravidianlangtech-1.26
Volume:
Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
Month:
March
Year:
2024
Address:
St. Julian's, Malta
Editors:
Bharathi Raja Chakravarthi, Ruba Priyadharshini, Anand Kumar Madasamy, Sajeetha Thavareesan, Elizabeth Sherly, Rajeswari Nadarajan, Manikandan Ravikiran
Venues:
DravidianLangTech | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
156–161
Language:
URL:
https://aclanthology.org/2024.dravidianlangtech-1.26
DOI:
Bibkey:
Cite (ACL):
Mesay Yigezu, Olga Kolesnikova, Grigori Sidorov, and Alexander Gelbukh. 2024. Habesha@DravidianLangTech 2024: Detecting Fake News Detection in Dravidian Languages using Deep Learning. In Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 156–161, St. Julian's, Malta. Association for Computational Linguistics.
Cite (Informal):
Habesha@DravidianLangTech 2024: Detecting Fake News Detection in Dravidian Languages using Deep Learning (Yigezu et al., DravidianLangTech-WS 2024)
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PDF:
https://aclanthology.org/2024.dravidianlangtech-1.26.pdf
Video:
 https://aclanthology.org/2024.dravidianlangtech-1.26.mp4